17 Result(s)
within
**n Hua Zhou
Computer Science
Computation by Abstract Devices
Pattern Recognition
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Chapter and Conference Paper
A Lower Bound Analysis of Population-Based Evolutionary Algorithms for Pseudo-Boolean Functions
Evolutionary algorithms (EAs) are population-based general-purpose optimization algorithms, and have been successfully applied in real-world optimization tasks. However, previous theoretical studies often empl...
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Chapter and Conference Paper
Selection Hyper-heuristics Can Provably Be Helpful in Evolutionary Multi-objective Optimization
Selection hyper-heuristics are automated methodologies for selecting existing low-level heuristics to solve hard computational problems. They have been found very useful for evolutionary algorithms when solvin...
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Chapter and Conference Paper
Approximate Bit-Vector Algorithms for Hashing-Based Similarity Searches
Similarity search, or finding approximate nearest neighbors, is becoming an increasingly important tool to find the closest matches for a given query object in large scale database. Recently, learning hashing-...
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Chapter and Conference Paper
Large Margin Distribution Learning
Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the...
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Chapter and Conference Paper
On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments
Sampling has been often employed by evolutionary algorithms to cope with noise when solving noisy real-world optimization problems. It can improve the estimation accuracy by averaging over a number of samples,...
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Chapter and Conference Paper
Using a Real-Time Top-k Algorithm to Mine the Most Frequent Items over Multiple Streams
Some applications such as sensor networks, internet traffic analysis, location-based services, and health measurements are always required for considering unbounded, fast, large-volumes, continuous, even for d...
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Chapter and Conference Paper
On Algorithm-Dependent Boundary Case Identification for Problem Classes
Running time analysis of metaheuristic search algorithms has attracted a lot of attention. When studying a metaheuristic algorithm over a problem class, a natural question is what are the easiest and the harde...
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Chapter and Conference Paper
Towards Analyzing Recombination Operators in Evolutionary Search
Recombination (also called crossover) operators are widely used in EAs to generate offspring solutions. Although the usefulness of recombination has been well recognized, theoretical analysis on recombination ope...
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Chapter and Conference Paper
Multi-information Ensemble Diversity
Understanding ensemble diversity is one of the most important fundamental issues in ensemble learning. Inspired by a recent work trying to explain ensemble diversity from the information theoretic perspective,...
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Chapter and Conference Paper
Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing
In the real-time series streaming environments, such as data analysis in sensor networks, online stock analysis, video surveillance and weather forecasting, similarity search, which aims at retrieving the simi...
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Chapter and Conference Paper
When Semi-supervised Learning Meets Ensemble Learning
Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; the latter attempts to achieve strong gen...
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Chapter and Conference Paper
Selective Ensemble under Regularization Framework
An ensemble is generated by training multiple component learners for a same task and then combining them for predictions. It is known that when lots of trained learners are available, it is better to ensemble ...
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Chapter and Conference Paper
A Prototype of Multimedia Metadata Management System for Supporting the Integration of Heterogeneous Sources
With the advances in information technology, the amount of multimedia metadata captured, produced, and stored is increasing rapidly. As a consequence, multimedia content is widely used for many applications in...
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Chapter and Conference Paper
Ensemble-Based Discriminant Manifold Learning for Face Recognition
The locally linear embedding (LLE) algorithm can be used to discover a low-dimensional subspace from face manifolds. However, it does not mean that a good accuracy can be obtained when classifiers work under t...
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Chapter and Conference Paper
Dependency Bagging
In this paper, a new variant of Bagging named DepenBag is proposed. This algorithm obtains bootstrap samples at first. Then, it employs a causal discoverer to induce from each sample a dependency model expressed ...
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Chapter and Conference Paper
Selective Ensemble of Decision Trees
An ensemble is generated by training multiple component learners for a same task and then combining their predictions. In most ensemble algorithms, all the trained component learners are employed in constituti...
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Chapter and Conference Paper
SOM Based Image Segmentation
Image segmentation plays an important role in image retrieval system. In this paper, a method for segmenting images based on SOM neural network is proposed. At first, the pixels are clustered based on their co...
